Self-organizing maps
Neural Network-Based Load Prediction for Highly Dynamic Distributed Online Games
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Clustering high-dimensional data using growing SOM
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Hierarchical Kohonenen net for anomaly detection in network security
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The parameterless self-organizing map algorithm
IEEE Transactions on Neural Networks
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The pervasiveness of computers in everyday life has already increased and keeps increasing the available digital data both in volume and variety/disparity. This large and dynamic availability of digital data is referred to as Big Data and is very promising in bringing forward new insights and knowledge. For obtaining these insights, the proper combination and processing of the data is required. However, the dynamicity and the increasing size of data start making their handling impossible for analysts and raise many concerns on the manner in which data will be processed from now on. Towards this direction, this paper proposes a tool that processes and combines disparate data in order to create insights regarding a future network load. In particular, the tool (based on the unsupervised machine learning technique of Self-Organizing Maps) builds knowledge on the network load that is encountered with respect to the date of interest, the location, the weather, and the features of the day (e.g., weekend, bank holiday, etc.). The obtained results reveal that the tool is capable of learning the traffic pattern and thus predicting the network load that will be encountered in the near or distant future given information for the above presented parameters with small deviations (up to 0.000553 in terms of Mean Square Error). Moreover, the tool maintains only the most representative data instances and thus reduces the data storage requirements with no loss of information.